---
title: "Iris Example"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
navbar:
- { title: "About", href: "https://example.com/about", align: right }
social: [ "twitter", "facebook", "menu" ]
source_code: embed
theme: yeti
---
```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(plotly)
library(ggplot2)
library(DT)
library(reshape2)
data("iris")
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
```
Description{dat-orientation=rows}
========================================================================
{width=50%}
***
#### Edgar Anderson's Iris Data
This famous (Fisher's or Anderson's) iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica. iris is a data frame with 150 cases (rows) and 5 variables (columns) named Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species.
#### References
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole. (has iris3 as iris.)
***
### Data Table{.no-title}
```{r}
datatable(iris, options = list(pageLength = 5))
```
>This table is generated by DataTables (https://rstudio.github.io/DT/).
Exploratory Data analytics (EDA)
========================================================================
Column {data-width=250}
-----------------------------------------------------------------------
Here is an area for some illustration
Column {data-width=500}
-----------------------------------------------------------------------
### Chart A: Distribution of Sepal Length
```{r}
ggplotly( ggplot(data=iris, aes(x=Sepal.Length, fill=Species))+geom_histogram(color='black', position = 'stack')+theme_bw()+labs(x='Sepal Length', y='Frequency', main='Distribution of Sepal Length') )
```
### Chart B: Distribution of Sepal Width
```{r}
ggplotly( ggplot(data=iris, aes(x=Sepal.Width, fill=Species))+geom_histogram(color='black', position = 'stack')+theme_bw()+labs(x='Sepal Width', y='Frequency', main='Distribution of Sepal Width') )
```
Column {data-width=500}
-----------------------------------------------------------------------
### Chart C: Distribution of Petal Length
```{r}
ggplotly( ggplot(data=iris, aes(x=Petal.Length, fill=Species))+geom_histogram(color='black', position = 'stack')+theme_bw()+labs(x='Petal Length', y='Frequency', main='Distribution of Petal Length') )
```
### Chart D: Distribution of Petal Width
```{r}
ggplotly( ggplot(data=iris, aes(x=Petal.Width, fill=Species))+geom_histogram(color='black', position = 'stack')+theme_bw()+labs(x='Petal Width', y='Frequency', main='Distribution of Petal Width') )
```
Setosa {data-navmenu="EDA on Species" data-icon="fa-lemon-o"}
========================================================================
Column {data-width=500}
-----------------------------------------------------------------------
###
Explaination
### corrplot
```{r}
corr <- round(cor(iris%>% filter(Species=='setosa')%>%select(-Species) ), 2)
ggplotly(ggplot(data = melt(corr), aes(x=Var1, y=Var2, fill=value))+geom_tile()+scale_fill_continuous(limits = c(0,1)))
```
Column {data-width=500}
-----------------------------------------------------------------------
### Chart 1: Sepal.Length vs. Sepal.Width
```{r}
ggplotly( ggplot(data = iris%>% filter(Species=='setosa'), aes(x=Sepal.Length, y=Sepal.Width))+geom_point()+geom_smooth(method = 'lm')+theme_bw() )
```
### Chart 2: Sepal.Length vs. Petal.Length
```{r}
ggplotly( ggplot(data = iris%>% filter(Species=='setosa'), aes(x=Sepal.Length, y=Petal.Length))+geom_point()+geom_smooth(method = 'lm')+theme_bw() )
```
### Chart 3: Sepal.Length vs. Petal.Width
```{r}
ggplotly( ggplot(data = iris%>% filter(Species=='setosa'), aes(x=Sepal.Length, y=Petal.Width))+geom_point()+geom_smooth(method = 'lm')+theme_bw() )
```
Column {data-width=500}{.tabset}
-----------------------------------------------------------------------
### Chart 4: Sepal.Width vs. Petal.Length
```{r}
ggplotly( ggplot(data = iris%>% filter(Species=='setosa'), aes(x=Sepal.Width, y=Petal.Length))+geom_smooth(method = 'lm')+geom_point()+theme_bw() )
```
### Chart 5: Sepal.Width vs. Petal.Width
```{r}
ggplotly( ggplot(data = iris%>% filter(Species=='setosa'), aes(x=Sepal.Width, y=Petal.Width))+geom_smooth(method = 'lm')+geom_point()+theme_bw() )
```
### Chart 6: Petal.Length vs. Petal.Width
```{r}
ggplotly( ggplot(data = iris%>% filter(Species=='setosa'), aes(x=Petal.Length, y=Petal.Width))+geom_smooth(method = 'lm')+geom_point()+theme_bw() )
```
Versicolor {data-navmenu="EDA on Species" data-icon="fa-pagelines"}
========================================================================
Column {data-width=500}
---------------------------------------------------------
### Correlation Plot
```{r}
corr <- round(cor(iris%>% filter(Species=='versicolor')%>%select(-Species) ), 2)
ggplotly(ggplot(data = melt(corr), aes(x=Var1, y=Var2, fill=value))+geom_tile()+scale_fill_continuous(limits = c(0,1)))
```
Column {data-width=250}
---------------------------------------------------------
### Chart 1: Sepal.Length vs. Petal.Length
```{r}
ggplotly( ggplot(data = iris%>% filter(Species=='versicolor'), aes(x=Sepal.Length, y=Petal.Length))+geom_point()+geom_smooth(method = 'lm')+theme_bw() )
```
### Chart 2: Petal.Length vs. Petal.Width
```{r}
ggplotly( ggplot(data = iris%>% filter(Species=='versicolor'), aes(x=Petal.Length, y=Petal.Width))+geom_point()+geom_smooth(method = 'lm')+theme_bw() )
```
Virginica {data-navmenu="EDA on Species" data-icon="fa-leaf"}
========================================================================
Column {data-width=250}
---------------------------------------------------------
***
some information
### Correlation Plot
```{r}
corr <- round(cor(iris%>% filter(Species=='virginica')%>%select(-Species) ), 2)
ggplotly(ggplot(data = melt(corr), aes(x=Var1, y=Var2, fill=value))+geom_tile()+scale_fill_continuous(limits = c(0,1)))
```
Column {data-width=250}
---------------------------------------------------------
### Chart 1: Sepal.Length vs. Petal.Length
```{r}
ggplotly( ggplot(data = iris%>% filter(Species=='versicolor'), aes(x=Sepal.Length, y=Petal.Length))+geom_point()+geom_smooth(method = 'lm')+theme_bw() )
```
### Chart 2: Sepal.Width vs. Petal.Width
```{r}
ggplotly( ggplot(data = iris%>% filter(Species=='versicolor'), aes(x=Sepal.Width, y=Petal.Width))+geom_point()+geom_smooth(method = 'lm')+theme_bw() )
```
Other {dat-orientation=rows}
==========================================================================
Row
-----------------------------------------------------------------------
### Articles per Day
```{r}
valueBox(5, icon = "fa-pencil")
```
### Comments per Day
```{r}
valueBox(5, icon = "fa-comments")
```
### Spam per Day
```{r}
spam = 15
valueBox(spam, icon = "fa-trash",color = ifelse(spam > 10, "warning", "primary"))
```
Row2
-----------------------------------------------------------------------
### Contact Rate
```{r}
rate <- 15
gauge(rate, min = 0, max = 100, symbol = '%', gaugeSectors(
success = c(80, 100), warning = c(40, 79), danger = c(0, 39)
))
```
### Average Rating
```{r}
rating <- 15
gauge(rating, min = 0, max = 50, gaugeSectors(
success = c(41, 50), warning = c(21, 40), danger = c(0, 20)
))
```
### Cancellations
```{r}
cancellations <- 15
gauge(cancellations, min = 0, max = 10, gaugeSectors(
success = c(0, 2), warning = c(3, 6), danger = c(7, 10)
))
```
Row3
-----------------------------------------------------------------------
Comments per Day